Despite measuring functional connectivity at over 1,000 locations, the appropriate "n" for the sample is 8 (or even smaller if you use the analysis I propose). So the analysis is likely under powered, that just means you have to be very cautious in how you interpret the findings: a significant result can be driven by one outlying person, and a non-significant result tells you very little.
Deciding on the functional effect of treatment is important. What will be the influence of treatment at time $C$? Some modeling effects of longitudinal treatment effect are are
- pre-post, such that at any time point after administration of treatment, the effect is expected to stay the same (expected response same at time $B$ or $C$),
- a growth or learning effect where the response at time $C$ is expected to be greater than at time $B$,
- a washout effect where the response at time $C$ is either between time $B$ and time $A$ or regresses completely to expected response at time $A$, or
- completely agnostic.
Once the effect of treatment is settled, I would approach this by fitting an ANCOVA. The control subjects cannot contribute to the analysis since you did not collect any post baseline measures. Reshape the data into a long format with one measure for each subject, each post-baseline time point, and each brain-region pair. Merge one-to-many the baseline values. Create indicators of time point, first post baseline vs. second post baseline.
The repeated measures at each site could be handled with a random intercept and possibly a random slope term to capture repeat measures at brain region pairs within subjects. You would include categorical effects of time, and adjust for baseline values. The interpretation of the time effects would be the average change from baseline.